Title: A robot-soldering workstation combined with the deep learning and the template matching technology

Authors: Guoyang Wan; Tinghao Yi; Guofeng Wang; Kaisheng Xing; Yunsheng Fan

Addresses: Anhui Polytechnic University, Wuhu, China ' University of Science and Technology of China, No. 96, JinZhai Road, Baohe District, Hefei, China ' Department of Marine Electrical Engineering, Dalian Maritime University, Linhai Road No. 1, Dalian, China ' Anhui Institute of Information Technology, Yonghe Road No. 1, Wuhu, China ' Department of Marine Electrical Engineering, Dalian Maritime University, Linhai Road No. 1, Dalian, China

Abstract: To improve the stability and precision in PCB soldering application, an unmanned robot workstation is proposed to solve the problem of automatic soldering of different model chips. In the first step, a novel 2D hand-eye calibration method is developed to acquire a high-precision transformation model from the coordinate system of the vision system to the robot working coordinate system. Then a robust classification and location method is developed to realise high precision positioning of PCB objects, which is based on deep neural network combining with the traditional template matching. The proposed method solves the problems of poor positioning accuracy and low recognition rate when traditional machine vision detects PCB objects with the same shape but different models. And the automation of chip soldering is realised. The experimental results show that the proposed algorithm displays excellent robustness.

Keywords: deep learning; template matching; hand-eye calibration; coordinate transformation; object detection.

DOI: 10.1504/IJICA.2022.125664

International Journal of Innovative Computing and Applications, 2022 Vol.13 No.4, pp.221 - 231

Received: 24 Aug 2020
Accepted: 23 Nov 2020
Published online: 26 Sep 2022 *

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